True Negative TN refers to samples that are correctly classified as negative in a binary classification problem.

For the binary classification problem, samples can be divided into four categories according to the combination of their true categories and the categories predicted by the learner, namely, true positive, false positive, true negative and false negative.

True and False are used to judge whether the result is correct or not, Positive and Negative are used to judge whether it is positive or negative. Therefore, the total number of samples = TP + FP + TN + FN

TN refers to samples that were originally negative and were correctly classified as negative.

Related terms: false positive, true negative, false negative, ROC curve, AUC curve